The collaborative frontiers of social networks and
opportunity recognition in convergent technologies.

Abstract:

Opportunity recognition has long been considered a key aspect in
entrepreneurship process. This leads to exploring the influential role
of social networks and information exchanges in opportunity recognition
in convergent technologies that will be a dominant force in the global
markets in the next decades. In this paper the effects of collaborative
frontiers of key social network players on opportunity recognition in
convergent technologies is studied. Further the effects of information
generated from convergent technology key players and the role of
knowledge structure as a potential mediator in opportunity recognition
process are discussed. Building on this framework, a model is developed
and a set of propositions is offered for the future research.

Subject:

Entrepreneurship
(Technology application)
Social networks
(Forecasts and trends)
New business enterprises
(Innovations)
New business enterprises
(Services)
New business enterprises
(Customer relations)

Opportunity recognition has long been considered as a crucial
aspect of the entrepreneurial process. (Ventakaraman, 1997; Gaglio,
1997; Hills, 1995). To understand the influential role of social
networks in entrepreneurial opportunity recognition in emergent
technologies first it is essential to review the essence of opportunity
recognition in prior literature. To date much prior opportunity
recognition research has in common that "innovation" and
"information" are two important elements in this process.

Opportunity recognition involves having specific information
regarding market, industry and technology conditions and other factors
(Ozgen & Baron, 2007) and the discovery of an innovative idea to
create new businesses (Schumpeter, 1934; Kirzner, 1973; Kirzner, 1997).
Opportunity recognition is also defined as an act of innovation in new
service, process and production (Schumpeter, 1934; 1943) that involves
seizing new marketable ideas (Kuratko & Hodgetts ,1998).

To date researchers applied various approaches in opportunity
recognition. Some focused on the role of active and systematic search
for information in the identification of entrepreneurial ideas and
innovations for new markets, products, services, methods or processes
(Schumpeter, 1942). In other words, entrepreneurs recognize
opportunities by scanning the environment for information and focusing
on markets, industry, customers and technological developments (Bhave,
1994; Reitan, 1997; Busenitz & Barney, 1996).). Prior research found
a strong link between the industry change, i.e. growth of sales, entry
barriers and manufacturing possibilities, or market structure such as
demographics and socio-cultural factors lead resigning of opportunities
and creation of new ventures (Drucker, 1985; Dean & Meyer, 1992).
Changing industry or market conditions may shift the demand and
production possibilities and lead to entrepreneurial inventions
(Timmons, 1999; Kuratko & Welsch, 2001; Dean & Meyer, 1992).
Especially, scanning the environment focusing on technology, consumer
economics, social values and governmental regulations play an important
role in the recognition of opportunities (Stevensen & Gumpert,
1985). Therefore, it was suggested that systematic search based on
specific information leads the discovery of entrepreneurial
possibilities (Shaver & Scott, 1991; Kaish & Gilad, 1991; Herron
& Sapienza, 1992; Fiet, et al., 2000, Fiet el al., 2002).

Some researchers suggested that possession of information or
alertness is critical in identifying and exploiting market opportunities
(Kirzner, 1973) and claimed that entrepreneurial opportunities exist
because people possess different information (Hayek, 1945). Possession
of different information leads individuals to discover entrepreneurial
possibilities because any given individual cannot identify all
opportunities (Kirzner, 1973). Therefore asymmetries of information
(Shane & Venkataraman, 2000) lead entrepreneurial innovations.

A stream of research applying a cognitive approach focused on the
way people think and process information (Baron & Markman, 1999) and
examined entrepreneurs who evaluate and discover opportunities and
explored the skills, cognitive processes (Baron, 1998). These
researchers found that "alertness", in other words, the way
entrepreneurs perceive information and process knowledge play a key role
in opportunity recognition. Therefore, opportunity recognition is a
cognitive process and in the mind of certain individuals and
opportunities are identified by being "alert" to possibilities
that the market presents (Woo et al., 1992; West & Myer, 1997; De
Koning, 1999).

Opportunity recognition is also defined as a joint function of
individual and the environment and entrepreneurs' interaction with
the environment shapes the evolution of ideas (Vesper, 1990; De Koning,
1999; Shane & Ventakaraman, 2000). In other words, opportunities are
both out there and also in the minds of certain individuals. Therefore,
opportunity recognition is a joint function of personal (background,
experience, education) and external factors (contextual and
environmental factors) (Singh, 2000).

Although different approaches were applied in prior opportunity
recognition studies much of all these earlier investigations have in
common that "innovation" and "information" play a
central role in this process. Consequently, the recognition of
entrepreneurial opportunities is linked to having specific information
regarding with market, industry and technology conditions and innovative
ideas. Since information and innovation are the essence of opportunity
recognition much research has investigated possible sources of these two
crucial factors in opportunity recognition. One potential source of
these two key elements that has received much attention has been social
networks.

Christensen and Peterson (1990) reported that in addition to other
factors social networks are often a source of venture ideas and are
positively linked to opportunity recognition for creating viable new
ventures. Entrepreneurs who used network sources identified
significantly more opportunities than those who developed their venture
ideas individually (Hills, et. all 1997). Social network contacts allow
entrepreneurs to have an access to a wide range of information (Singh,
Hills, Hybels, & Lumpkin, 1999; (Shane & Cable, 2002) and create
linkages between resources and opportunities (Singh, 2000). Access to
diverse commercial social networks updates entrepreneurs with changes in
industry and market conditions from customers, government regulations
and competitors (Almeida & Kogut, 1999; Shane & Cable, 2002;
Gargiulo & Benassi, 2000) and underpins entrepreneurs'
knowledge base (Bozeman & Mangematin, 2004) leading to innovations
and idea generation and recognition of opportunities (De Carolis &
Saparito, 2006; Ozgen & Baron, 2007).

Social networks and opportunity recognition in convergent
technologies

Social network ties are especially crucial in recognizing
opportunities and enhancing innovation capabilities in technology
domain. Network alliances increase innovative performance of high tech
industries (Soh & Roberts, 2006) and play an influential role in
idea generation, transferring knowledge and opportunity recognition
(Greve & Salaff, 2003, Dimov, 2007). Therefore, it is logical to
assume that a fundamental understanding of the social networking
strategies in opportunity recognition process in emerging technologies
will be as of timely and worthwhile for entrepreneurship researchers.

Convergent technologies are emergent technologies that combine a
set of technologies and various scientific disciplines. The term
"convergent technologies" indicates the convergence of new
emerging technologies, such as nanotechnology, biotechnology,
information technology and new human technologies. It is expected that
convergent technologies will revolutionize science and technology in the
next decade and lead to dramatic developments in a variety of fields
such as medical science, material science, electronics, military,
healthcare, chemical plants, transportation, pharmaceuticals,
manufacturing, agriculture, energy, environmental management, etc. (Roco
& Bainbridge, 2001).

As the emergent technologies will create remarkable advances over
the globe in the next decade it is considered worthwhile to further
explore opportunity recognition in convergent technologies. Innovation
and information have long been considered as the essence of opportunity
recognition and social networks are found as one possible source of
these two crucial elements. Although prior research contributed our
understanding of opportunity recognition immensely to-date how socially
generated information lead to opportunity recognition within the complex
and multifaceted system of convergent technologies has not, as yet, been
carefully explored.

The multifaceted nature and complex structure of convergent
technologies heavily depend upon the accumulated knowledge of multitude
disciplines, such as science, engineering and technology, across
boundaries between disciplines and a vibrant set of industry, government
and university activities (Roco & Bainbridge, 2001). Since the
complexity of methods and procedures increase in convergent technologies
the need for collaborations among a variety of partnerships with
different skills and capabilities increase. Therefore we suggest that it
is crucial to investigate key social network players and information
generated from these sources in transferring knowledge as predictors of
innovation and new ideas (Sorenson & Waguespack, 2005) in
opportunity recognition research in convergent technologies.

Previous entrepreneurship research found that teams with
multidisciplinary background lead to idea generation and innovation
especially in high tech industries (Alves, Marques, Saur, Marques, 2007;
Kamm, Shuman, Seeger & Nurick, 1990; Timmons, 1994). Currently
convergent technologies are driven by academic research. Consequently,
academic social network that consists of multidisciplinary teams of
scientists, engineers and technicians may be particularly critical in
opportunity recognition in convergent technology domain (Roco &
Bainbridge, 2001).

As the process of developing new products and processes get
progressively more complex in convergent technologies, collaboration of
cross-functional team members in academia will be crucial in
facilitating resource accumulation, idea generation and creating
linkages to entrepreneurial ideas.

An academic social network consisting of highly trained people with
expertise in diverse disciplines such as physics, chemistry, biology,
materials and engineering will increase diffusion of information,
broaden knowledge and research base (Roco & Bainbridge, 2001).
Social network of academicians will foster new knowledge creation and
access to wide range of scientific and technical information that will
lead radical innovations, which stimulate opportunity recognition and
increase entrepreneurial behavior.

It was found that information and prior knowledge add significant
insights into entrepreneurial discovery. Prior knowledge of a particular
field provides individuals the capacity to recognize entrepreneurial
opportunities (Shane, 2000). Therefore social network of academicians
with different background and expertise will help entrepreneurs combine
diverse pieces of knowledge to recognize an entrepreneurial opportunity.
Based on this reasoning we propose that:

P1. The greater the extent to which entrepreneurs in academia with
different background and expertise team up in new projects, the more
likely they will be to discover opportunities for new ventures in
convergent technology.

Information generated from industry-academia collaboration

Convergent technology domain involves interaction among scientists
and diverse links between academic and industrial participants.
Therefore an effective use of social networking strategies constitutes a
valuable tool to interweave diverse links and bridge the gap between
academic and industrial participants (Adler & Kwon, 2002) and is
considered a critical aspect in opportunity recognition within the
ecosystem of convergent technologies.

To date it was found that developing social capital is a major
challenge for academic entrepreneurs in universities compared to
entrepreneurs in commercial environments (Mustar et al., 2006; Lockett
& Wright, 2005; Nicolaou & Birley, 2003). Accessing social
networks is crucial to commercialize technological innovation (Delmar
& Shane, 2004). Yet at present much convergent technologies research
is constrained to university and national lab environment (Wolff, 2006).
Convergent technology innovations are driven by academia and therefore
to be able to identify opportunities with commercial market applications
(Lockett, Wright, & Franklin, 2003) and capture the full potential
of nanotech innovations entrepreneurs in universities need to broaden
their scientific networks in university environment to commercial
networks in industry environment. Commercialization potential of
innovations in convergent technologies across a wide range of projects
and creating new materials, products and techniques for industrial
manufacture require coordinated efforts and interaction between
university and industry sector.

Collaboration between academic and industry participants is a
crucial path in recognition of opportunities in convergent technologies.
Developing partnerships and forming social networks between academic
institutions and the industry private sector will strengthen ties
between academic researchers and the industry sector and create a
broader approach in cross fertilization of ideas. Industry-academia
social network will support easing the transfer of mutual intellectual
input and feedback and fostering establishment of convergent
technologies industry hubs (PMSEIC, 2005).

We suggest that entrepreneurs in academia can often obtain valuable
information from persons that they meet or contact in the industry
(these individuals might include but not restricted to venture
capitalists, investors, suppliers, manufacturers, producers, etc.) on
changing trends in the market, social behavior patterns, consumer
economics, changing structure of the industry and current market
circumstances. Further social network collaborations between private
sector and academia will also assist academicians in making contacts in
the field, such as with financiers, suppliers, and customers, keeping
industry specific knowledge up-to-date and accessing to informed
decision-making. Therefore, social networks formed between university
and industry private sector will help entrepreneurs in convergent
technologies scan the environment and identify opportunities by using
different types of information about the market.

Social networks formed between large firms and universities enhance
industry-wide applications ideas of convergent technologies and this
will create new demand and supply curves for new products, methods and
processes replacing existing ones. As it is suggested in creative
destruction theory innovative processes in new products, production or
organizational methods, markets, sources of input and market structures
(Schumpeter, 1934) through applications of convergent technologies
projects will induce implementation of new applications in a range of
industries and bring innovations to the market. Increase demand for new
technologies and methods will enhance opportunity recognition in various
markets and boost entrepreneurial behavior and innovations in convergent
technologies.

Companies investing in university research funds through joint
projects gain access to new technological developments and innovation
(Chesbrough, 2003). Social networking with academia and private sector
will ease the transfer of the nanotech research results into commercial
application and facilitate entrepreneurial opportunity recognition.
Further, university spin-outs have long been considered as an important
source in generating technology development and transferring
university-invented technology to industry and markets (Roberts, 1991).

Universities are increasingly becoming the center of convergent
technology development. Therefore, through private sector and academia
network universities transfer nanotech innovations and more in-depth
reflections of emerging technological developments back into the market
and industry in the form of new entrepreneurial venture opportunities.
Universities on the other hand through joint development agreements will
be given a range of initiatives to support and strengthen the academic
research along with financial support and access to information on
possible commercial market applications. This leads to the next
propositions:

P2. The greater the extent to which entrepreneurs in academia
collaborate with the private sector in industry as a source of
information, the more likely they will be to identify opportunities for
new ventures in convergent technologies.

Government generated information

Opportunity recognition in emerging technologies is closely linked
with the government collaboration. Prior research findings show that
university and government research institutions play a significant role
in advancing scientific activities (Miyazaki & Islam, 2007). In this
study "government" refers to "government generated
information and funding for convergent technology projects".

When new technologies start developing the infrastructure required
for that technology needs to be developed as well including regulations,
legitimation and resource fundings (Van de Ven, 1993). That means, the
legal requirements of entrepreneurship in convergent technologies not
only include patents, licensing, intellectual property rights and
commercial application issues but also a wide range of law and policy
requirements and regulations that engage environment, safety and social
responsibility (Reinert, Andrews, & Keenan, 2006). Therefore
information obtained from government on regulations and standardization
of the new technology will be helpful for entrepreneurs in
prioritization and coordination efforts to bridge knowledge gaps in
recognition of opportunities.

Further, convergent technology initiatives involve high cost and
start-up funding initially and could create an obstacle in launching new
research programs. Prior research found that more entrepreneurial firms
were founded in states with high science and technology and economics
initiatives than those without such initiatives (Woolley & Rottner,
2008). Therefore, we expect government generated information and funding
for any convergent technology projects will not only give an access to
research funding for promoting new technology and build confidence in
the capital market but also ease understanding of the regulatory
frameworks (PMSEIC, 2005) for the new technology.

In sum, government generated information and funding for convergent
technology projects will facilitate innovations and rapid
commercialization of research results and lead to meaningful progress in
recognition of opportunities (NNI, 2008). This leads to the next
proposition:

P3. The greater the extent to which entrepreneurs receive
government related information and funding for convergent technology
projects the more likely they will be to discover opportunities in
convergent technologies.

KNOWLEDGE STRUCTURE AS A POTENTIAL MEDIATOR

Knowledge structure is a cognitive factor that is derived from
"schemas", i.e. mental frameworks that give structure to and
organize information in memory and enable individuals to perceive
connections between events and knowledge (Baron, 2006).

Schema theorists imply that individuals remember information that
fits well with the schema (Hastie & Kumar, 1979). Applying pattern
recognition theory and the prototype model, Baron (2006) proposed that
mental frameworks play an essential role in opportunity recognition
because individuals more likely notice information relevant with their
existing schemas. Baron (2006) suggested that training, previous
experience and learning shape individuals' mental frameworks, which
influence their perception of external world. Thus, it might be easier
to notice and identify opportunities through information relevant to
individuals' existing schemas than information irrelevant to
existing schemas (Baron, 2006).

For instance, individuals with better-developed schemas related to
an industry can "access signals from information services or
channels, to which they subscribe, based on their prior knowledge"
(Fiet, Piskounov, & Gustavson, 2000, pp.198). Their background may
help them to filter signals from the environment and transform them into
information, carry out data handling, adapt complex technical
developments and utilize available information processing.

Prior research findings suggest that alertness schemas play an
important role in opportunity recognition Gaglio and Katz, 2001) and the
more developed entrepreneurs' schemas for knowledge in a particular
field, the more likely they use this information in opportunity
recognition (Ozgen & Baron, 2007).

Extending previous research finding on the importance of schemas in
opportunity recognition we suggest that further studying the extent of
knowledge structure, referring the way in which individuals organize
knowledge in "three specific schema layers", will be valuable
in understanding the opportunity recognition process. Knowledge
structure refers to three basic schemas called "declarative",
procedural" and "structural" under which knowledge may be
organized. Declarative knowledge includes information about concepts,
names and things. Procedural knowledge contains how to steps to be able
to do a task; and structural knowledge refers to deeper understanding of
a material or a concept (Grotzer, 2002). We suggest that studying the
extent of entrepreneur's knowledge structure will provide us a
better understanding on "how" entrepreneurs employ information
that they obtain from various sources in recognizing opportunities in
convergent technologies. We predict that the better developed
declarative, procedural and structural knowledge of an entrepreneur in a
given technological field the better and more organized knowledge
structure the individual has. As a result, better developed knowledge
structure will ease processing information and informed individuals are
more likely to identify stimuli relevant with their existing knowledge.

Based on this reasoning we propose that the impact of social
network collaboration mechanism on opportunity recognition is partially
mediated by the strength of entrepreneurs' knowledge structure for
convergent technology related information. Entrepreneurs who have better
developed knowledge structure in convergent technologies are more likely
recognize new business opportunities through social network
collaboration compared to those who have less developed knowledge
structure in convergent technologies. These individuals with better
developed knowledge structure will better apply and utilize information
generated from these social sources in recognition of opportunities for
viable new ventures.

P4. The effects of social network mechanism on opportunity
recognition in convergent technologies will be partially mediated by
knowledge structure.

The proposed model is shown in Figure 1 on the following page.

SUGGESTIONS FOR METHODOLOGY

As the field of convergent technologies is still developing it is
suggested to study nascent entrepreneurs in convergent technologies in
opportunity recognition research. Nascent entrepreneurs are those who
are involved in independent business start-up efforts and/or trying to
start a new venture (Delmar & Davidson, 2000) either alone or with
others (Reynolds, 1999). This involves any behavior associated with
starting a new firm such as earning money on sales, doing market
research and saving money to start business (Delmar &Davidson 2000).
Individuals who qualify as nascent entrepreneurs expect to be owners or
part owners of a new venture; have been active in trying to start-up a
new venture in the past 12 months and the effort is still in the
start-up or gestation phase (Reynolds, 1999. pp.170).

Opportunity recognition measure could be adapted from one used in
previous research (Singh et al., 1999) that includes entrepreneurs'
self assessments and also from various other quantitative and verifiable
methods such as "the number of companies started; number of patents
held; number of opportunities recognized and cross-validation of
entrepreneurs' self-assessments by persons who know them well and
are familiar with their actual success in identifying
opportunities". (Ozgen & Baron, 2007, p. 24). Knowledge
structure measure could be adopted from previous research (Grotzer,
2002). The other measures could be developed by the researchers as
needed.

Structural Equation Modeling Analysis is suggested to analyze the
data as it is a reliable straightforward method of dealing with multiple
and interrelated dependence relationships simultaneously while providing
statistical efficiency (Hair, 1998).

[FIGURE 1 OMITTED]

FURTHER THOUGHTS FOR FUTURE OPPORTUNITY RECOGNITION RESEARCH

At present convergent technologies are still in infancy.
Consequently, initial costs of new applications are expected to be high
in the beginning. The high cost of research and start-up packages may
pose some difficulty in recognition of opportunities for cutting edge
ideas and transferring these opportunities into new research programs.
Therefore, until it builds up impetus initially convergent technology
initiatives require very high funding and investment.

As much convergent technology research is carried out in university
environment at present convergent technology researchers in academia use
information stem from similar academic or scientific environment such as
scientific conferences, journals, reports and workshops and thereby are
constrained with diversity of information sources (Wolff, 2006). The
main challenge in convergent technologies includes forming intense
social network collaborations among industry, academia, government and
other sectors that help to build a knowledge base and convert knowledge
into commercial outcomes, in other words transferring research results
from academia or laboratories to industry (NNI, 2008). The multifaceted
challenges in opportunity recognition in convergent technologies involve
the collaboration of many disciplines, managing diverse social networks,
working across many different fields, integrating different perspectives
among partners, handling massive diverse volume of information and
assessing business implications and commercial results of nanotech
innovations (Bean, Chapas, Collins & Kingon 2005).

Exploring various other information sources with commercial market
applications could expose convergent technologies to many possible
application areas. Scholars suggest further studying collaborative
efforts between industry and academia that lead to identifying
commercial needs to project for large-scale market impact opportunities
(Osman, Rardon, Friedman & Vega, 2006). Further, opportunity
recognition researchers could explore the influence of diversity of
information coming from various social network sources.

A longitudinal study on academic entrepreneurs' social network
ties revealed that differences in the human capital and prior business
experience of academic entrepreneurs could play a significant role in
forming social network ties with industry (Mosey & Wright, 2007).
Regardless of academic discipline the lack of prior business experience
could create structural holes in the social connections between
university and private sector and form a barrier for academicians in
building effective social network ties with investors and managers in
industry (Mosey & Wright, 2007) . Therefore, having business
experience or developing human capital could be regarded as a
significant factor for academicians in emerging technologies. Hence, the
lack of prior business experience in academia could pose difficulty in
forming social network ties with industry and recognition of
entrepreneurial opportunities with commercial market applications.

U.S. National Science and Technology Council Report proposed that
fostering R&D infrastructure, promoting vital research areas,
encouraging and developing the scientific and technical human capital
are among the major challenges in convergent technologies domain (NSTC,
2000). Scholars suggest implementing considerable changes in academia to
offer courses, training and degrees in convergent technologies and build
interdisciplinary centers of expertise to provide depth and broad
approaches in social network formation which lead to recognition of
opportunities for viable new ventures (Greg, 2004).

In conclusion, the conceptual model presented here is only a step
towards understanding the collaborative frontiers of social networks in
opportunity in convergent technologies. It is hoped that the ideas
suggested here may provide insights for the future opportunity
recognition research in convergent technologies.

Busenitz, L.W., and Barney, J.B. (1997). Differences between
entrepreneurs and managers in large organizations: Biases and heuristics
in strategic decision-making. Journal of Business Venturing, 12, 9-30.

Johannisson, B. (1990). Building an Entrepreneurial career in a
mixed economy: Need for social and business ties in personal networks.
Presented to the Academy of Management Annual Meeting. San Francisco,
CA.

Sorenson, O., Waguespack, D. M. (2005). Research on social networks
and the organization of research and development: An introductory essay.
Journal of Engineering and Technology Management, 22(1), 1-7.